时间：2022年7月11日（周一）16.00

平台：腾讯在线会议 会议ID：159-816-348 

主讲人：李勇

李勇教授，中国人民大学计量经济学和金融学教授(博导)，首批教育部青年长江学者， 香港中文大学统计学博士，新加坡管理大学金融学博士后。现任中国人民大学经济学院副院长。他主要的研究方向是贝叶斯金融计量经济学，量化投资，资产管理。李勇教授在《Journal of Econometrics》、《经济研究》、《管理世界》等杂志共发表文章近50篇, 出版学术专著一部，编著一部。 曾获教育部自然科学二等奖，人文社会科学三等奖，入选教育部新世纪人才计划，北京市青年优秀人才计划。

内容摘要：

Hypothesis testing based on p-values has been criticized in recent years. The conventional Bayes factors (BFs) have been tipped as possible replacements of p-values. However, conventional BFs suffer from several theoretical and practical difficulties. For example, the conventional BFs are not well-defined under improper priors and they subject to Jeffreys-Lindley-Bartlett's paradox when proper but vague priors are used. Moreover, they are difficult to compute for many models. In this paper, we propose to compare the sampling distributions of the posterior-test-based statistics for hypothesis testing. Two posterior-test-based statistics are considered, namely the posterior version of likelihood ratio (LR) test and the posterior version of Wald test. Under some regularity conditions, we establish the consistency property of the new method. We also show how the proposed method can address the problems in p-values and those in the conventional BFs. The advantages of the proposed method are highlighted using several simulation studies and empirical financial studies.

主持人：

张学勇

中央财经大学研究生工作部部长、研究生院院长、金融学院教授、中国资产管理研究中心主任

主办单位：

中央财经大学金融学院

中国资产管理研究中心